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Articles by Tuhin Paul
AWS Cognito User Pools vs. Identity Pools in LangGraph
Demystify AWS Cognito User Pools vs. Identity Pools for secure, multi-agent LangGraph RAG. Build enterprise-grade AI with fine-grained access control.
Validating and Authorizing JWTs in a Serverless Multi-Agent RAG Backend
Learn how to validate JWTs with API Gateway and enforce claim-based authorization, tenant isolation, and PHI controls in a serverless LangGraph RAG backend.
Building a Secure Enterprise Multi-Agent RAG API with AWS API Gateway, Cognito, and LangGraph
Secure enterprise RAG API with AWS API Gateway, Cognito, and LangGraph for identity, data isolation, and reliable LLM answers.
Retries, DLQs, and Poison-Message Handling in Enterprise LangGraph RAG
Master AI resilience in LangGraph RAG with Retries, DLQs, and Poison-Message Handling for robust enterprise AI systems.
Designing Idempotent Consumers in Event-Driven AI Systems
Design idempotent consumers for event-driven AI systems to prevent duplicate LLM calls, API triggers, and state corruption with LangGraph.
SQS, SNS, and EventBridge in a Multi-Agent LangGraph RAG System
Build resilient, scalable, and cost-effective enterprise AI with LangGraph and AWS SQS, SNS, EventBridge for event-driven RAG systems.
Handling Lambda Concurrency Spikes and Downstream Throttling in Enterprise LangGraph RAG
Tame LangGraph RAG concurrency spikes and downstream throttling with SQS, Lambda concurrency limits, LLM fallbacks, and defensive API calls.
Why AWS Lambda is the Superior Compute Engine for Enterprise LangGraph RAG
Discover why AWS Lambda excels over ECS Fargate for enterprise LangGraph RAG, offering instant scaling and cost efficiency for AI workloads.
Handling API Payload Truncation in Enterprise RAG
API payload truncation isn’t a limitation to hack around it’s a design surface for enterprise RAG reliability.
Building Resilient Enterprise RAG: Implementing LLM Fallback Mechanisms with LangGraph
By leveraging LangGraph, we can elegantly orchestrate complex fallback mechanisms. We bypass the LLM entirely for deterministic tasks using rule-based logic, and we protect our users from downtime by cascading to smaller, faster models when our primary endpoints degrade.
Converting Messy Documents to Markdown in Enterprise RAG
If you have built an enterprise Retrieval-Augmented Generation (RAG) system, you already know the dirty secret of the industry: The LLM is rarely the bottleneck; the data ingestion pipeline is. Enterprises do not store their knowledge in neat, pre-formatted JSON files. Knowledge is trapped in 50-page PDFs with complex tables, nested DOCX onboarding guides, and legacy TXT configuration logs. If you naively extract text from these files and chunk them using a simple character-count splitter, your RAG system will suffer from "context fragmentation." The LLM will receive a chunk of text without knowing what document it came from, what chapter it belongs to, or what the parent topic was.
Building an Enterprise Near-Real-Time RAG System with LangGraph
Build an enterprise near-real-time RAG system with LangGraph for instant data insights. Handle complex documents and ensure accuracy.
Building an Agentic RAG Pipeline with LangGraph to Eliminate Retrieval Errors
Eliminate RAG retrieval errors with LangGraph's agentic pipelines. Enhance accuracy, reduce hallucinations, and boost ticket deflection.
Navigating Vector Store Trade-offs and Building Agentic Workflows with LangGraph
Explore vector store trade-offs (Pinecone, Chroma, Milvus, pgvector) and build secure, agentic RAG workflows with LangGraph for enterprise.
Citation Grounding for Compliance-Heavy Industries using LangGraph
Build a citation-grounded RAG system with LangGraph for compliance-heavy industries, ensuring AI traceability and trust.
Detecting Context Poisoning and Irrelevant Injection using LangGraph
Build a LangGraph-based RAG pipeline that detects context poisoning, irrelevant documents, and indirect prompt injection before generating responses.
Tuning Chunking, Retrieval, and LangGraph for Institutional-Grade AI
Unlock institutional-grade AI for finance. Tune chunking, hybrid search, and LangGraph for accurate, hallucination-free RAG.
The Role of Reranking in RAG and an End-to-End LangGraph Implementation
Unlock RAG's potential with reranking! Learn its critical role and build an end-to-end LangGraph implementation for precise, production-ready LLM applications.
Fixing DevOps RAG Failures Using LangGraph Reflection
Fix DevOps RAG failures with LangGraph reflection. Solve generation bottlenecks by enabling LLMs to self-correct for accurate, concise answers.
Diagnosing and Fixing Poor Retrieval Relevance in Enterprise RAG with LangGraph
Fix enterprise RAG retrieval relevance with LangGraph. Overcome chunking, semantic gaps, poor queries, and metadata issues for accurate AI answers.
Diagnosing and Fixing Poor Retrieval Relevance in Enterprise RAG with LangGraph
Fix enterprise RAG retrieval relevance with LangGraph. Learn to diagnose and solve common issues like chunking, semantic gaps, and query formulation.
Hybrid RAG with LangGraph: Vector, Keyword & Metadata Retrieval in Action
Master hybrid RAG with LangGraph: Combine vector, keyword, and metadata retrieval for production-ready, auditable AI answers.
Selecting the Right Embedding Model for a New Domain
Empirically select the best embedding model for specialized domains using LangGraph. Optimize RAG performance beyond general knowledge.
